Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments.

中文翻译：

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用于汽车雷达应用的光谱Radon-Fourier变换

快速傅立叶变换（FFT）是雷达应用中广泛使用的基本信号处理算法之一。Radon-Fourier变换（RFT）可以看作是一种FFT概括，可以克服其某些局限性。这项工作得出了三种基于频谱RFT（SRFT）的方法，以解决多输入多输出汽车雷达的主要挑战。首先，推导了两种基于SRFT的方法，通过共同减轻目标在距离和到达方向上的迁移以及通过多驻留集成处理来增加最大目标检测距离，这增加了雷达相干集成时间，而不会损害其检测更新率。下一个，